2018 MultiTurnQAARNNContextualApproa

• (Mensio et al., 2018) ⇒ Martino Mensio, Giuseppe Rizzo, and Maurizio Morisio. (2018). “Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-oriented Systems.” In: Companion Proceedings of the The Web Conference 2018. ISBN:978-1-4503-5640-4 doi:10.1145/3184558.3191539

Subject Headings: Belief-Desire-Intention Agent System; Multi-Agent System; Recurrent Neural Network; Question Answering System

Cited By

• http://scholar.google.com/scholar?q=%222018%22+Multi-turn+QA%3A+A+RNN+Contextual+Approach+to+Intent+Classification+for+Goal-oriented+Systems
• http://dl.acm.org/citation.cfm?id=3184558.3191539&preflayout=flat#citedby

Quotes

Abstract

QA systems offer a human friendly interface to navigate through knowledge, which can range from encyclopedic to domain-specific. Generally, a QA system is designed to provide an answer to a specific question once (so-called single turn) and state-of-the-art systems reach nowadays robust performance in such a scenario. However, most of the interactions with QA systems are based on multiple handshakes of question / answer pairs, where the human being refines the questions further, while the system can collect the necessary information and generate a compelling final answer through multiple turns. In this paper, we investigate and experiment a multi-turn QA system that is suited to work given a particular domain of knowledge and configurable goals. Our approach models the entire dialogue as a sequence of turns, i.e. questions and answers, using a Recurrent Neural Network which is firstly trained to understand natural language, classifying entities and intents using prior knowledge of domain-specific interactions, and provide answers according to the domain used as background knowledge. We have compared our approach with state-of-the-art sequence-based intent classification using a well-known and standardized gold standard observing an increase of 17.16% of F1. Results show the robustness of the approach and the competitive results motivate the adoption in multi-turn QA scenarios.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 MultiTurnQAARNNContextualApproa	Giuseppe Rizzo Martino Mensio Maurizio Morisio			Multi-turn QA: A RNN Contextual Approach to Intent Classification for Goal-oriented Systems				10.1145/3184558.3191539		2018